建立 BrS 與 LQT3 中 SCN5A 突變位點之預測模型

Abstract

SCN5A encodes a cardiac sodium channel. Its mutations are associated with Brugada Syndrome (BrS) and Long QT Syndrome Type 3 (LQT3). Both diseases are often neglected by the clinicians because they are difficult to diagnose. Hundreds of non-synonymous variants have been identified in SCN5A; however, the underlying mechanism and the relationship between the genotype and phenotype remain unclear. A new approach that helps to screen and prioritize identified mutations is beneficial for researchers to identify a novel pathogenic mutation in this high-throughput sequencing era. Therefore, we aim to study and analyze the characteristics of SCN5A variants in order to evaluate the possibility of these mutations developing into BrS or LQT3. In this study, 4 prediction algorithms were used to predict whether a variant is pathogenic or benign. The algorithms includes: Sorts Intolerant From Tolerant (SIFT), Protein Variation Effect Analyzer (PROVEAN), Polymorphism Phenotyping v2 (PolyPhen2) and Genomic Evolutionary Rate Profiling++ (GERP++). Several variants (BrS N=425, LQT3 N=136) were collected from literatures and published reports. Furthermore, Estimated Predictive Values (EPV) is used to evaluate the frequency of one variant in a rare disease, such as BrS or LQT3. Therefore, for each variant, EPV was calculated and all variants were classified into different groups based on the protein structures and exon information. The results demonstrated that higher prediction performances can be obtained when at least 3 prediction algorithms agreed on pathogenicity. For example, the EPVs increased from 56% to 75% and 60% to 83% in the domains of Pfam-B3701 and Na transmembrane in BrS, respectively. In general, the results showed that the proposed approach was able to discriminate case-derived variants and general-population-derived variants. Based on the filtered variants, a prediction model was developed to evaluate potential risk for each variant. In addition, the associations between the SCN5A domains and the two diseases, BrS and LQT3, were evaluated. Intriguingly, the results showed that a variant in domain II (DII) transmembrane has a higher possibility that can develop into BrS. Similarly, a variant in the C-terminal may have a higher chance turning into LQT3. In conclusion, a probability model that integrates EPV and 4 prediction algorithms was developed in this study in order to classify variants identified in SCN5A and evaluate the chance that such variants may lead to BrS or LQT3.